Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization
The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of servic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9006805/ |
| _version_ | 1818616679681228800 |
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| author | Mohamed K. Hussein Mohamed H. Mousa |
| author_facet | Mohamed K. Hussein Mohamed H. Mousa |
| author_sort | Mohamed K. Hussein |
| collection | DOAJ |
| description | The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes. |
| first_indexed | 2024-12-16T16:53:38Z |
| format | Article |
| id | doaj.art-68d0cf6ab2384410ab4a3323b40d739f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:53:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-68d0cf6ab2384410ab4a3323b40d739f2022-12-21T22:23:56ZengIEEEIEEE Access2169-35362020-01-018371913720110.1109/ACCESS.2020.29757419006805Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony OptimizationMohamed K. Hussein0https://orcid.org/0000-0002-4635-4969Mohamed H. Mousa1https://orcid.org/0000-0002-0733-2919Faculty of Computers and Informatics, Suez Canal University, Ismailia, EgyptFaculty of Computers and Informatics, Suez Canal University, Ismailia, EgyptThe current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes.https://ieeexplore.ieee.org/document/9006805/Fog computingInternet of Thingsquality of servicetask offloading and scheduling |
| spellingShingle | Mohamed K. Hussein Mohamed H. Mousa Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization IEEE Access Fog computing Internet of Things quality of service task offloading and scheduling |
| title | Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization |
| title_full | Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization |
| title_fullStr | Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization |
| title_full_unstemmed | Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization |
| title_short | Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization |
| title_sort | efficient task offloading for iot based applications in fog computing using ant colony optimization |
| topic | Fog computing Internet of Things quality of service task offloading and scheduling |
| url | https://ieeexplore.ieee.org/document/9006805/ |
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